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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- recall
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- precision
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model-index:
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- name: albert-base-v2-Tweet_About_Disaster_Or_Not
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# albert-base-v2-Tweet_About_Disaster_Or_Not
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4396
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- Accuracy: 0.9200
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- F1: 0.8043
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- Recall: 0.7890
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- Precision: 0.8202
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
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| 0.3598 | 1.0 | 143 | 0.3025 | 0.8795 | 0.7495 | 0.8650 | 0.6613 |
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| 0.234 | 2.0 | 286 | 0.2899 | 0.8989 | 0.7784 | 0.8523 | 0.7163 |
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| 0.1557 | 3.0 | 429 | 0.3424 | 0.9156 | 0.7904 | 0.7637 | 0.8190 |
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| 0.0871 | 4.0 | 572 | 0.4189 | 0.9182 | 0.7901 | 0.7384 | 0.8495 |
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| 0.0517 | 5.0 | 715 | 0.4396 | 0.9200 | 0.8043 | 0.7890 | 0.8202 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1
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- Datasets 2.9.0
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- Tokenizers 0.12.1
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runs/.DS_Store
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version https://git-lfs.github.com/spec/v1
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oid sha256:c93c60cb0c7aa376165171c0235012e8c4b28399def4af3d34ae806ef7a9283b
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size 6148
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